U.S. patent number 10,668,824 [Application Number 15/740,887] was granted by the patent office on 2020-06-02 for method for calculating a setpoint for managing the fuel and electricity consumption of a hybrid motor vehicle.
This patent grant is currently assigned to RENAULT s.a.s. The grantee listed for this patent is RENAULT s.a.s. Invention is credited to Thierry Denoeux, Atef Gayed, Xavier Jaffrezic, Abdel-Djalil Ourabah, Benjamin Quost.
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United States Patent |
10,668,824 |
Ourabah , et al. |
June 2, 2020 |
Method for calculating a setpoint for managing the fuel and
electricity consumption of a hybrid motor vehicle
Abstract
A method that calculates a setpoint for managing the fuel and
electricity consumption of a hybrid motor vehicle includes: a)
acquiring, by a navigation system on board the hybrid motor
vehicle, a route to be traveled; b) dividing the route into
consecutive portions; c) assigning attributes that characterize
each portion; d) determining, for each of the portions, a curve or
a map that links each fuel consumption value of the hybrid motor
vehicle over the portion to a charge or discharge value of the
traction battery; e) determining an optimal point of each curve or
map, which makes it possible to minimize the fuel consumption of
the hybrid motor vehicle over the entire route and to completely
discharge the traction battery by the end of the route; and f)
producing an energy management setpoint in accordance with the
coordinates of the optimal points.
Inventors: |
Ourabah; Abdel-Djalil (Paris,
FR), Jaffrezic; Xavier (Guyancourt, FR),
Gayed; Atef (Marly la Ville, FR), Quost; Benjamin
(Compiegne, FR), Denoeux; Thierry (Compiegne,
FR) |
Applicant: |
Name |
City |
State |
Country |
Type |
RENAULT s.a.s |
Boulogne-Billancourt |
N/A |
FR |
|
|
Assignee: |
RENAULT s.a.s
(Boulogne-Billancourt, FR)
|
Family
ID: |
54199835 |
Appl.
No.: |
15/740,887 |
Filed: |
June 15, 2016 |
PCT
Filed: |
June 15, 2016 |
PCT No.: |
PCT/FR2016/051444 |
371(c)(1),(2),(4) Date: |
April 09, 2018 |
PCT
Pub. No.: |
WO2017/001740 |
PCT
Pub. Date: |
January 05, 2017 |
Prior Publication Data
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|
|
|
Document
Identifier |
Publication Date |
|
US 20180281620 A1 |
Oct 4, 2018 |
|
Foreign Application Priority Data
|
|
|
|
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Jul 2, 2015 [FR] |
|
|
15 56271 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60L
15/2045 (20130101); B60L 50/16 (20190201); B60W
10/06 (20130101); B60W 10/08 (20130101); B60W
10/26 (20130101); B60W 20/12 (20160101); B60L
3/12 (20130101); Y02T 10/7005 (20130101); Y02T
10/64 (20130101); B60W 2556/50 (20200201); Y02T
90/16 (20130101); Y02T 90/161 (20130101); Y02T
10/72 (20130101); B60L 2240/443 (20130101); Y02T
10/62 (20130101); Y02T 10/6286 (20130101); Y02T
10/645 (20130101); Y02T 10/70 (20130101); Y02T
10/56 (20130101); Y02T 10/705 (20130101); B60L
2240/441 (20130101); Y02T 10/84 (20130101); B60L
2240/423 (20130101); Y02T 10/7077 (20130101); B60L
2240/421 (20130101); B60W 2050/0041 (20130101); Y02T
10/7044 (20130101); B60L 2260/54 (20130101); B60W
2552/20 (20200201); Y02T 10/40 (20130101); B60L
2240/12 (20130101); Y02T 10/7283 (20130101); B60L
2260/52 (20130101); Y02T 10/7072 (20130101) |
Current International
Class: |
B60W
20/12 (20160101); B60W 10/08 (20060101); B60W
10/06 (20060101); B60L 3/12 (20060101); B60L
15/20 (20060101); B60W 10/26 (20060101); B60L
58/12 (20190101); B60L 50/16 (20190101); B60L
58/13 (20190101); B60W 50/00 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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|
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|
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2 907 745 |
|
May 2008 |
|
FR |
|
2 988 674 |
|
Oct 2013 |
|
FR |
|
2015/059536 |
|
Apr 2015 |
|
WO |
|
Other References
International Search Report dated Aug. 22, 2016, in
PCT/FR2016/051444 filed Jun. 15, 2016. cited by applicant .
French Search Report dated May 24, 2016 in French Application No.
15 56271 Filed Jul. 2, 2015. cited by applicant.
|
Primary Examiner: Tissot; Adam D
Assistant Examiner: Pipala; Edward J
Attorney, Agent or Firm: Oblon, McClelland, Maier &
Neustadt, L.L.P.
Claims
The invention claimed is:
1. A calculation method for calculating a setpoint for managing
fuel and electricity consumption of a hybrid motor vehicle
comprising at least one electric motor supplied with electricity by
a traction battery, and an internal combustion engine supplied with
fuel, the calculation method comprising: a) acquiring, by a
navigation system, a journey to be made; b) dividing said journey
into successive sections, the dividing including selecting a length
of each section of the journey as a maximum length of the journey
over which a predetermined attribute is constant, the predetermined
attribute being one of a gradient of the section, a speed category
of the section, and a highway category of the section; c)
acquiring, for each section, additional attributes characterizing
said section; d) selecting, for each of said sections and taking
into account the predetermined attributes and additional
attributes, from among a plurality of predetermined relationships
linking fuel consumption values to electrical energy consumption
values, a relationship linking the fuel consumption of the hybrid
motor vehicle over the section to the electrical energy
consumption; e) determining an optimal point of consumption in each
of the selected relationships such that the set of optimal points
minimize the fuel consumption of the hybrid motor vehicle over the
entire journey and maximize the discharge of the traction battery
at an end of said journey; f) developing an energy management
setpoint throughout the journey, according to coordinates of said
optimal points; and wherein, prior to step b), dividing the journey
into a plurality of adjacent segments, and each of the segments
corresponds to a part of the journey that extends between two
highway intersections, and wherein the journey is re-divided in
step b) such that segments in which the predetermined attribute is
identical are combined into the same section.
2. The calculation method as claimed in claim 1, in which the
predetermined relationships are curves or maps linking fuel
consumption values of the internal combustion engine to charge or
discharge values of the traction battery.
3. The calculation method as claimed in claim 1, in which, in step
d), the relationship is selected regardless of a gradient of said
section, and in which, prior to step e), provision is made for a
correction step d1) for correcting said relationship taking into
account said gradient.
4. The calculation method as claimed in claim 3, in which said
correction step d1) includes shifting each point of the
relationship so as to modify, at a constant charge or discharge
value, the fuel consumption by a value according to the
gradient.
5. The calculation method as claimed in claim 1, in which, in step
d), the relationship is selected regardless of the electricity
consumption of auxiliary devices which are separate from the
electric motor and which are powered by the traction battery, and
in which, prior to step e), provision is made for a correction step
d2) for correcting said relationship taking into account said
electricity consumption of the auxiliary devices.
6. The calculation method as claimed in claim 5, in which said
correction step d2) consists in shifting each point of the
relationship so as to modify, at a constant fuel consumption value,
the charge or discharge by a value according to said electricity
consumption of the auxiliary devices.
7. The calculation method as claimed in claim 6, in which steps
d)-f) are repeated when the electricity consumption of the
auxiliary devices varies substantially during the journey.
8. The calculation method as claimed in claim 1, further
comprising: storing in a memory the predetermined relationships and
a table associating with each value of the predetermined attributes
and additional attributes a probability that the section is
associated with one of the predetermined relationships, in which,
in step d), provision is made for each section: to determine, based
on said table, taking into account the values of the predetermined
attributes and additional attributes associated with the section, a
sum of the probabilities that the section belongs to one of the
predetermined relationships, and to select the relationship having
a highest probability sum.
9. The calculation method as claimed in claim 1, in which the
predetermined relationships are curves and each of the curves is
defined as a second order polynomial, for which the variations in
charge and discharge of the traction battery are bounded between a
minimum threshold and a maximum threshold.
10. The calculation method as claimed in claim 9, in which said
polynomial has two invariable coefficients.
11. The calculation method as claimed in claim 1, in which, in step
e), the optimal consumption point of the relationship associated
with each section is determined by means of an optimization
algorithm.
12. The calculation method as claimed in claim 1, in which the
length of each section is selected such that the gradient of the
section, the speed limit of the section, and the highway category
of the section are all constant over the length.
13. The calculation method as claimed in claim 1, in which the
additional attributes include a speed limit of the section, a mean
speed of the section, an instantaneous speed of the section, a
length of the section, a mean radius of curvature of the section,
and a number of lanes of the section.
Description
TECHNICAL FIELD TO WHICH THE INVENTION IS RELATED
The present invention relates generally to rechargeable hybrid
vehicles.
It relates more particularly to a calculation method for
calculating a setpoint for managing the fuel and electricity
consumption of a hybrid motor vehicle comprising at least one
electric motor supplied with electricity by a traction battery, and
an internal combustion engine supplied with fuel.
The invention finds a particularly advantageous application in
long-range hybrid electric vehicles, i.e., in vehicles capable of
traveling a distance greater than 10 kilometers with the aid of
their electric motor alone.
TECHNOLOGICAL BACKGROUND
A rechargeable hybrid vehicle comprises a conventional combustion
drivetrain (with an internal combustion engine and a fuel tank) and
an electric drivetrain (with an electric motor and a traction
battery notably capable of charging at a power outlet).
Such a hybrid vehicle is capable of being drawn along just by its
electric drivetrain alone, or by its combustion drivetrain alone,
or even simultaneously by its two electric and combustion
drivetrains. It is also possible to recharge the traction battery
by taking advantage of the power developed by the internal
combustion engine, or also by recovering the kinetic energy
developed by the motor vehicle on braking.
Due to ignorance of the vehicle's future journey, the strategy
currently implemented for using one or other of the drivetrains
consists in systematically beginning by discharging the traction
battery at the start of the journey until reaching a level of
minimum energy, then using the combustion drivetrain. In this way,
when the driver makes short journeys and has regular opportunities
to recharge the traction battery, they use the electric drivetrain
to the maximum, which reduces the pollutant emissions of the
vehicle.
This strategy does not, however, always ensure minimum fuel
consumption. This is notably the case when the user begins a
journey via a freeway part and ends it with a part in town. Indeed,
the use of the electric drivetrain is unsuited to the freeway since
the traction battery discharges very quickly thereon, and the use
of the combustion drivetrain is unsuited to town since the internal
combustion engine's performance is lower in town than on the
freeway.
In order to overcome this drawback, document U.S. Pat. No.
8,825,243 discloses how to construct an "ideal" discharge curve of
the battery on a journey prediction known to a navigation system,
this curve being constructed so that the charge state of the
battery reaches its minimum permissible value only at the end of
the journey, then how to control the hybrid system on this journey
prediction so as to best follow this discharge curve. The drawback
of such a solution is that in case of significant diversity of
highway conditions on the journey, e.g. the simple but very common
case when starting on a first section in town, then continuing on a
second freeway section and finally ending on a third section in
town, then the journey is performed in a non-optimal manner from
the point of view of energy consumption.
The use of the combustion drivetrain in town further proves less
pleasant for the driver than that of the electric drivetrain.
Finally, legislation sometimes prevents the use of the internal
combustion engine in town, so that the driver then no longer has
access to town.
SUBJECT MATTER OF THE INVENTION
In order to remedy the aforementioned drawbacks of the prior art,
the present invention provides for overcoming ignorance of the
future journey by exploiting the data from the navigation system
embedded in the vehicle.
More particularly, the invention provides a calculation method as
defined in the introduction, in which provision is made for the
steps of:
a) acquiring, by means of a navigation system, a journey to be
made,
b) dividing said journey into successive sections,
c) acquiring, for each section, attributes characterizing said
section,
d) for each of said sections and taking into account its
attributes, selecting, from among a plurality of predetermined
relationships linking fuel consumption values to electrical energy
consumption values, a relationship linking the fuel consumption of
the hybrid motor vehicle over the section to its electrical energy
consumption,
e) determining an optimal point of consumption in each of the
selected relationships, such that the set of optimal points
minimize the fuel consumption of the hybrid motor vehicle over the
entire journey and maximize the discharge of the traction battery
at the end of said journey, and
f) developing an energy management setpoint throughout the journey,
according to the coordinates of said optimal points.
Thus, thanks to the invention, it is possible to determine at what
times either the electric motor or the internal combustion engine
should be used in order to best reduce the fuel consumption of the
vehicle over the journey that it has to make.
By way of example, it will be possible to give priority to using
the combustion drivetrain on the freeway, where its performance is
best, and using the electric drivetrain in town, where its
performance and its pleasantness are optimal.
It will also be possible to improve the performance of the internal
combustion engine by relieving it thanks to the electric motor at
the most unfavorable operating points.
Other advantageous and non-restrictive features of the calculation
method in conformity with the invention are as follows: the
predetermined relationships are curves or maps linking fuel
consumption values of the internal combustion engine to charge or
discharge values of the traction battery; in step d), the
relationship is selected regardless of the gradient of said
section, and prior to step e), provision is made for a correction
step d1) for correcting said relationship taking into account said
gradient; said correction step d1) consists in shifting each point
of the relationship so as to modify, at a constant charge or
discharge value, the fuel consumption by a value according to the
gradient; in step d), the relationship is selected regardless of
the electricity consumption of auxiliary devices which are separate
from the electric motor and which are powered by the traction
battery, and prior to step e), provision is made for a correction
step d2) for correcting said relationship taking into account said
electricity consumption of the auxiliary devices; said correction
step d2) consists in shifting each point of the relationship so as
to modify, at a constant fuel consumption value, the charge or
discharge by a value according to said electricity consumption of
the auxiliary devices; if, during the journey, the electricity
consumption of the auxiliary devices varies substantially, steps d)
and following are repeated; a memory storing the predetermined
relationships and a table associating with each attribute value a
probability that the section is associated with one or other of the
predetermined relationships, in step d), provision is made for each
section to determine, thanks to said table, taking into account the
values of the attributes associated with this section, the sum of
the probabilities that the section belongs to one or other of the
predetermined relationships, and to select the relationship having
the highest probability sum; since the relationships are curves,
each curve is defined as a second order polynomial, for which the
variations in charge and discharge of the traction battery are
bounded between a minimum threshold and a maximum threshold; said
polynomial has two invariable coefficients; in step b), each
section is defined as being a portion of maximum length of the
journey that comprises at least one invariable attribute throughout
its length; said invariable attribute over each section is chosen
from the following list: gradient of the section, characteristic
speed of vehicles over the section and category assigned to the
section by the navigation system; in step (e), the optimal
consumption point of the relationship associated with each section
is determined by means of an optimization algorithm.
DESCRIPTION OF THE DRAWINGS
The following description in conjunction with the appended
drawings, given by way of non-restrictive examples, will elucidate
the substance of the invention and how it may be implemented.
In the appended drawings:
FIG. 1 is a table illustrating the attribute values characterizing
sections of a journey that a vehicle has to make;
FIG. 2 is a table illustrating the parameters of reference curves
characterizing the sections of the journey to be made;
FIG. 3 is a graph illustrating the distribution of specific
consumption curves acquired during test runs;
FIG. 4 is a graph illustrating multiple reference curves;
FIG. 5 is a table associating with each attribute value assigned to
a section, a probability that this section is associated with one
or other of the reference curves in FIG. 4;
FIG. 6 is a graph illustrating the corrections to be made to a
reference curve, taking into account the electricity consumption of
auxiliary devices of the vehicle;
FIG. 7 is a graph illustrating the corrections to be made to a
reference curve, taking into account the gradient of the section of
the corresponding journey; and
FIG. 8 is a graph illustrating different points for each reference
curve associated with each section and a curve passing through the
optimal points of these reference curves.
DETAILED DESCRIPTION
Conventionally, a motor vehicle comprises a chassis which notably
supports a powertrain, bodywork elements and passenger compartment
elements.
In a rechargeable hybrid vehicle, the powertrain comprises a
combustion drivetrain and an electric drivetrain.
The combustion drivetrain notably comprises a fuel tank and an
internal combustion engine supplied with fuel by the tank.
The electric drivetrain comprises a traction battery and one or
more electric motors supplied with electricity by the traction
battery.
The motor vehicle here also comprises a power outlet for charging
the traction battery locally, e.g. on a home power grid or any
other power grid.
The motor vehicle also comprises auxiliary devices, which are here
defined as electrical devices powered by the traction battery.
These auxiliary devices may include the air conditioning motor, the
electric window motors, or the geolocation and navigation
system.
This geolocation and navigation system conventionally comprises an
antenna for receiving signals relating to the geolocalized position
of the motor vehicle, a memory for storing a map of a country or a
region, and a screen for illustrating the vehicle's position on
this map.
Here, the case will be considered where this screen is a touch
screen for allowing the driver to enter information thereon. Of
course, it could be otherwise.
Finally, the geolocation and navigation system comprises a
controller for calculating a journey to be made taking into account
the information entered by the driver, the map stored in its
memory, and the motor vehicle's position.
The motor vehicle 1 further includes an Electronic Control Unit (or
ECU), here referred to as the computer, notably making it possible
to control the two aforementioned drivetrains (notably the powers
developed by the electric motor and by the internal combustion
engine).
In the context of the present invention, this computer is connected
to the controller of the geolocation and navigation system, so that
these two elements may communicate information therebetween.
Here, they are connected together via the main inter-unit
communication network of the vehicle (typically via the CAN
bus).
The computer includes a processor and a memory unit (hereinafter
referred to as the memory).
This memory records data used as part of the method described
below.
It notably records a table of the type illustrated in FIG. 5 (which
will be described in detail later in this disclosure).
It also records a computer application, consisting of computer
programs including instructions, the execution of which by the
processor allows the computer to implement the method described
below.
As a preliminary, several concepts will be defined here which are
used in the disclosure of the method described below.
Thus, the term "journey" may be defined as being a path that the
motor vehicle has to take from a departure station to arrive at an
arrival station.
This arrival station, the goal of the journey, will be considered
as being provided with a charging station for recharging the
traction battery via the power outlet fitted to the vehicle.
Each journey may be split into "adjacent segments" or into
"adjacent sections".
The notion of segments will be that natively used by the controller
fitted in the geolocation and navigation system.
In practice, each segment corresponds to a part of the journey
which extends between two highway intersections. To define the
shortest or the fastest journey, the controller will therefore
determine via which highway segments the journey must pass.
The notion of sections is different. It will be thoroughly
described in detail in the rest of this disclosure. To simplify,
each section of the journey corresponds to a part of the journey
over which the characteristics of the highway do not substantially
evolve. By way of example, the journey could thus be split into
multiple sections over each of which the speed limit is
constant.
These sections are characterized by parameters here referred to as
"attributes". The following are examples of attributes for
characterizing each section.
A first attribute will be the "highway category FC". The
controllers fitted in the geolocation and navigation systems
generally use this kind of category for distinguishing the various
types of highways. Here, this category may take an integer value
between 1 and 6. An attribute equal to 1 may correspond to a
freeway, an attribute equal to 2 may correspond to a national
highway, etc.
A second attribute will be the "gradient RG" of the section,
expressed in degrees or as a percentage.
The third, fourth, fifth and sixth attributes will be related to
characteristic speeds of the vehicles using the section.
The third attribute will be the "speed category SC" of the section.
The controllers fitted in the geolocation and navigation systems
also generally use this kind of category for distinguishing the
various types of highways. Here, this category may take an integer
value between 1 and 6. An attribute equal to 1 may correspond to a
high-speed highway (over 120 kph), an attribute equal to 2 may
correspond to an expressway (between 100 and 120 kph), etc.
The fourth attribute will be the "speed limit SL" over the
section.
The fifth attribute will be the "mean speed SMS" found on the
section (the value of which is derived from a statistical
measurement made on each highway).
The sixth attribute will be the "instantaneous speed TS" found on
the section (the value of which is derived from an information
system on the state of the traffic in real time).
The seventh attribute will be the "length LL" of the section. The
eighth attribute will be the "mean radius of curvature LC" of the
section.
The ninth attribute will be the "number of lanes NL" of the section
in the direction of travel taken by the vehicle.
In the following disclosure, these nine attributes will be used to
characterize each section of the journey.
As a variant, each section of the journey may be characterized by a
smaller or greater number of attributes.
Moreover, the state of energy SOE of the traction battery will be
defined as being a parameter for characterizing the remaining
energy in this traction battery. As a variant, another parameter
may be used such as the state of charge SOC of the battery or any
other parameter of the same type (internal resistance of the
battery, voltage at the battery terminals, etc.).
The charge or discharge .DELTA.SOE of the traction battery will
then be considered equal to the difference between two energy
states considered at two different times.
The "specific consumption curve" of the vehicle is then defined on
a section considered as being a curve that associates with each
fuel consumption value CC of the vehicle a charge or discharge
value .DELTA.SOE of the traction battery. Indeed, over a determined
section, it is possible to estimate what the vehicle's fuel
consumption CC will be (in liters per kilometer traveled) and the
charge or discharge .DELTA.SOE of the traction battery (in
watt-hours per kilometer). These two values will be linked by a
curve, since they will vary according to whether the electric
drivetrain or the combustion drivetrain is used to propel the
vehicle.
Since there are an infinite number of specific consumption curves,
finally the "reference curves" are defined as being particular
specific consumption curves, the characteristics of which will be
well known and which will make it possible to approximate each
specific consumption curve. Put another way, as will appear more
clearly later in this disclosure, not a specific consumption curve,
but rather a reference curve (that which will be the best
approximation of the specific consumption curve) will be associated
with each journey section.
The method, which is implemented jointly by the controller of the
geolocation and navigation system and by the vehicle's computer, is
a calculation method for calculating a setpoint for managing the
fuel and electricity consumption of the vehicle.
This method consists more precisely in determining how, over a
predefined journey, it will be necessary to use the electric
drivetrain and the combustion drivetrain in such a way as to best
reduce the vehicle's fuel consumption as well as the pollutant
emissions thereof.
According to one particularly advantageous feature of the
invention, the method includes the following six main steps:
acquiring a journey to be made, dividing said journey into
successive adjacent sections T.sub.i, acquiring, for each section
T.sub.i, attributes FC, SC, SL, TS, RG, LL NL, SMS characterizing
this section T.sub.i, determining, for each of the sections
T.sub.i, taking into account the attributes FC, SC, SL, TS, RG, LL
NL, SMS of this section T.sub.i, a relationship (referred to here
as the reference curve CE.sub.j) linking each fuel consumption
value CC of the hybrid motor vehicle over the section to a charge
or discharge value .DELTA.SOE of the traction battery, determining
an optimal point P.sub.i of each reference curve CE.sub.j for
minimizing the fuel consumption of the hybrid motor vehicle over
the entire journey and obtaining a complete discharge of the
traction battery at the end of said journey, and developing an
energy management setpoint according to the coordinates of said
optimal points P.sub.i.
These six successive steps are described in detail later in this
disclosure.
The first step consists in acquiring the journey that the motor
vehicle has to make.
This step may be carried out by the controller embedded in the
geolocation and navigation system.
This step is then implemented conventionally.
Thus, when the driver uses the touch screen of the geolocation and
navigation system for defining an arrival station, the controller
of this system calculates the journey to be made, notably according
to the routing parameters selected by the driver (fastest journey,
shortest journey, etc.).
At this stage, it may be noted that the method must be
reinitialized as soon as the vehicle makes a different journey from
that defined by the geolocation and navigation system.
As a variant, this first step may be carried out differently.
Thus, it will be possible to dispense with the driver entering the
arrival station on the touch screen. For this, the controller may
detect the driver's habits and automatically deduce the arrival
station therefrom.
For example, when the driver makes the same journey every day of
the week to work, this journey may be automatically acquired
without the driver having to enter any information on the touch
screen of the geolocation and navigation system.
At the end of this first step, the controller embedded in the
geolocation and navigation system knows the vehicle's journey,
which is then composed of a plurality of adjacent segments,
remembering that they each extend between two highway
intersections.
The second step consists in dividing the journey into sections
T.sub.i.
The advantage of re-dividing the journey not into segments but into
sections is firstly to reduce the number of subdivisions of the
journey. Indeed, it often happens that the attributes of two
successive segments are identical. If these two successive segments
were processed separately, the calculation time would be multiplied
pointlessly. By collecting identical segments together within the
same section, it will be possible to reduce the calculation
time.
Another advantage is that the characteristics of the highway over
the same segment may vary substantially (one part of the segment
may correspond to a highway with a zero gradient and another part
of this segment may correspond to a highway with a considerable
gradient). Here, the aim is to divide the journey into sections
over each of which the characteristics of the highway remain
homogeneous.
Each section T.sub.i will be defined here as being a portion of the
journey that comprises at least one invariable attribute throughout
its length.
This attribute may consist of the gradient RG and/or the speed
category SC and/or the highway category FC.
Here, this step will be implemented by the controller embedded in
the geolocation and navigation system. For this purpose it will
split the journey into sections T.sub.i of maximum lengths over
which the aforementioned three attributes (RG, SC, FC) are
constant.
At the end of this second step, the controller has thus defined N
sections.
The third step consists in acquiring the attributes of each section
T.sub.i.
When one of the attributes varies over the section considered, it
is the mean value of this attribute over the entire section that
will be considered.
In practice, this third step is performed in the following way.
First of all, the controller embedded in the geolocation and
navigation system informs the computer that a new journey has been
calculated. The computer then asks for the attributes of each
section to be sent, in the form, for example, of a table of the
type illustrated in FIG. 1.
The controller then acquires the attributes of each section in the
following way.
It calculates one part thereof, notably the length LL of the
section.
It reads another part thereof in the memory of the geolocation and
navigation system, notably the highway category FC, the gradient
RG, the speed category SC, the speed limit SL, the mean speed SMS,
the mean radius of curvature LC, and the number of lanes NL.
A last part of these attributes is communicated via another device,
notably the instantaneous speed TS that the information system on
the state of real-time traffic communicates thereto.
The controller then transmits all this information to the vehicle's
main computer, via the CAN bus.
The advantage of using the controller embedded in the geolocation
and navigation system rather than the vehicle's main computer for
carrying out the first three steps lies in reducing the amount of
information to be transmitted to the computer via the CAN bus.
Indeed, by merging the adjacent segments of the journey that have
the same attributes, the volume of the transmitted data is reduced,
which speeds up data transmission via the CAN bus.
Upon receiving the information, the computer implements the
following steps.
The fourth step then consists, for each of the sections T.sub.i, in
determining from among the reference curves CE.sub.j recorded in
the memory of the computer that which will make it possible to best
estimate the energy consumption (in fuel and in current) of the
vehicle over the section T.sub.i considered.
This step then makes it possible to pass from a characterization of
each section by attributes, to a characterization by energy
cost.
In the course of this fourth step, the computer will use the table
TAB illustrated in FIG. 5, which is recorded in its memory.
As shown in this FIG. 5, this table TAB depicts lines that each
correspond to a value (or to a range of values) of an attribute. It
depicts columns each corresponding to one of the reference curves
CE.sub.j. In the illustrated example, it is considered that the
computer's memory stores M reference curves CE.sub.j, with M equal
to eleven here.
In FIG. 5, the boxes of the table TAB are left empty since the
values that they comprise will depend on the vehicle's
characteristics.
In practice, this table TAB will be stored in the computers memory
with values in each of these boxes.
These values will be probability values (between 0 and 1)
corresponding to the probability that each attribute value
corresponds to one or other of the reference curves CE.sub.j.
By way of example, if the highway category FC of a section T.sub.i
has a value equal to 2, it may be read in the table that the
probability that this section is well characterized in terms of
energy cost by the reference curve CE1 will be equal to a.sub.1,
that the probability that this section is well characterized in
terms of energy cost by the reference curve CE2 will be equal to
a.sub.2, etc.
It should be noted that the gradient RG and length LL values have
not been, by design, used in this table TAB.
At this stage, the computer may then read off each probability
value corresponding to the value of each attribute of the section
T.sub.i considered.
In the illustrated example, where it is considered that the
attribute FC is equal to 2, that the attribute SC is equal to 6,
that the attribute SL is equal to 30, that the attribute NL is
equal to 2, that the attribute SMS is between 60 and 80 and that
the attribute TS is between 40 and 60, the computer reads off the
values denoted by a.sub.1 to a.sub.11, b.sub.1 to b.sub.11, c.sub.1
to c.sub.11, d.sub.1 to d.sub.11, e.sub.1 to e.sub.11, and f.sub.1
to f.sub.11.
The computer then sums up the probabilities that the section
T.sub.i considered is well characterized in terms of energy cost by
each of the eleven reference curves CE.sub.j.
In the illustrated example, the computer accordingly sums up the
values denoted by a.sub.1 to f.sub.1, then a.sub.2 to f.sub.2,
etc.
Finally, the computer determines which of the eleven sums gives the
highest result.
Then, it considers that the reference curve CE.sub.j with which
this high probability sum is associated is that which best
characterizes the section T.sub.i in terms of energy cost.
The computer may then acquire in its memory the values of the
parameters characterizing this reference curve CE.sub.j.
At this stage of the disclosure, the focus is more specifically on
the way in which these reference curves are obtained and
modeled.
For each vehicle model (or for each engine/motor model, or for each
set of automobile models, or for each set of engine/motor models),
it is necessary to carry out a large number of test runs (or
simulation of test runs) on different geolocalized sections of
highway.
These test runs make it possible to determine the fuel consumption
and electricity consumption of the vehicle over different sections,
the attributes of which are known. For this, the vehicle is driven
multiple times over each section each time increasing the share of
the traction developed by the electric motor.
It is then possible to generate a specific consumption curve CCS
for each section. These specific consumption curves are the type of
curves illustrated in FIG. 4.
It may be observed on each of these curves that the more electrical
energy is used (i.e., a .DELTA.SoE<0) the more the fuel
consumption drops until it reaches 0 during a run using the
electric drivetrain exclusively. Conversely, the more it is sought
to recharge the battery via the combustion engine (.DELTA.SoE>0)
the more the fuel consumption increases. Finally, it will be
recalled that each specific consumption curve CCS describes the
mean energy consumption of the vehicle for the situation of a
horizontal highway run (zero gradient) without electricity
consumption by the auxiliary devices.
These test runs make it possible to find as many specific
consumption curves CCS as there are tested sections.
Each specific consumption curve CCS may be modeled by a second
order polynomial for which the variations in charge and discharge
.DELTA.SOE of the traction battery are bounded between a minimum
threshold .DELTA.SOE.sub.min and a maximum threshold
.DELTA.SOE.sub.max, which may be written:
.PSI..times..DELTA..times..times..PSI..times..DELTA..times..times..PSI..-
DELTA..times..times..di-elect
cons..DELTA..times..times..DELTA..times..times. ##EQU00001## with
.PSI..sub.0, .PSI..sub.1, .PSI..sub.2 the coefficients of the
polynomial.
As the curves in FIG. 4 show, in order to simplify this model, it
may be estimated that the two coefficients .PSI..sub.1, .PSI..sub.2
are identical from one curve to another. It may also be observed
that the minimum threshold .DELTA.SOE.sub.min depends on the three
coefficients of the polynomial. Thus, only the coefficient
.PSI..sub.0 and the maximum threshold .DELTA.SOE.sub.max vary. It
is therefore these two values that make it possible to characterize
each specific consumption curve CCS.
FIG. 3 then illustrates points the coordinates of which correspond
to these two variables .PSI..sub.0 and .DELTA.SOE.sub.max. It shows
the distribution of the specific consumption curves CCS obtained
during conducted test runs. Here, it is considered that these
points are distributed in eleven distinct areas. Each area is then
defined by its barycenter.
Thus, as has been disclosed above, in the method, it is not the
specific consumption curve that would exactly correspond to the
section considered that is acquired, but rather it is one of the
eleven reference curves, the variables .PSI..sub.0 and
.DELTA.SOE.sub.max of which correspond to the barycenter of one of
these eleven areas, that is considered.
At this stage of the method, each section T.sub.i is then defined
as shown in FIG. 2 by the aforementioned parameters .PSI..sub.0,
.PSI..sub.1, .PSI..sub.2, .DELTA.SOE.sub.min, .DELTA.SOE.sub.max,
and by the length LL.sub.i of each section T.sub.i and by its
gradient RG.sub.i.
As explained above, the selected energy curve CE.sub.i does not
take into account either the gradient of the section T.sub.i or the
electricity consumption of the auxiliary devices (air conditioning
motor, etc.).
In order to take into account the gradient of each section T.sub.i
a correction step is provided for correcting each reference curve
CE.sub.i according to the gradient RG.sub.i.
As clearly shown in FIG. 7, this correction step simply consists in
shifting the reference curve CE.sub.i associated with the section
T.sub.i upward or downward (i.e., with a constant charge or
discharge .DELTA.SOE), by a value according to the gradient
RG.sub.i.
It will be understood indeed that when the section of highway
considered climbs uphill, fuel consumption will be higher than
initially expected. Conversely, when the section of highway
considered goes downhill, the fuel consumption will be lower than
initially expected.
In addition, during braking phases, it will be possible to recover
more electricity downhill than uphill.
In practice, the correction step will consist in correcting the
parameter .PSI..sub.0 according to the following formula:
.PSI..sub.0'=.PSI..sub.0+KRGi,
with K a coefficient in the value depending on the vehicle model
considered and its characteristics (by way of example, K=0.01327
lkm.sup.-1 may be considered here).
In order to take into account the electricity consumption of the
auxiliary devices, a second correction step is provided for
correcting each reference curve CE.sub.i according to the
electrical power P.sub.aux consumed by these auxiliary devices.
Here it should be noted that the electrical power value P.sub.aux
considered is the value that can be measured at the time of the
calculations. In this method, the assumption is therefore made that
the electrical power consumed will remain substantially constant
during the journey. If ever the computer were to detect a large
variation in this electrical power over a significant duration
(e.g. because the air conditioning is switched on), it could be
programmed to restart the method at this step in order to take into
account the new electrical power value P.sub.aux.
More precisely, the method could be reinitialized to this second
correction step if the difference between the electrical power
considered in the calculations and that measured had to remain
above a threshold (e.g. 10%) over a duration above a threshold
(e.g. 5 minutes).
As clearly shown in FIG. 6, the second correction step simply
consists in shifting the reference curve CE.sub.i associated with
the section T.sub.i to the left (i.e., at a constant fuel
consumption), by a value according to the electrical power
P.sub.aux.
It will be understood indeed that when the electrical devices are
used, the battery charge will be slower than expected and the
discharge of this battery will be faster than expected.
In practice, the correction step will consist in shifting the
reference curve CE.sub.j by a value E.sub.AUX calculated from the
following formula:
##EQU00002##
where v represents the mean speed over the section (in kph). This
value may be supplied directly by the geolocation and navigation
system, by estimating that it will be equal to the value of the
traffic speed or to the statistical mean speed or to the speed
limit.
The fifth step of the method then consists in determining, on each
reference curve CE.sub.j the optimal point P.sub.i for minimizing
the fuel consumption of the hybrid motor vehicle over the entire
journey and obtaining a complete discharge of the traction battery
at the end of said journey.
This step is performed here by means of a type A* optimization
algorithm. This is an algorithm known in the prior art and
therefore will not be described here in detail. However, its
operation may be explained briefly.
For this, reference will be made to FIG. 8.
It is seen there that for each section, a series of crossing points
is plotted by energy states SOE parallel to the ordinate axis, with
an abscissa equal (in kilometers) to the distance between the
departure station and the final point of the section. Each point of
this line corresponds to an attainable energy state SOE deduced
from the reference curve CE.sub.j associated with this section. The
energy states SOE space is discretized in a finite number of
points.
The ordinate of each point is then equal to the energy state SOE of
the traction battery that would remain at the end of the section if
the vehicle was driven according to the corresponding point of the
reference curve CE.sub.j, taking into account the charge or
discharge applied to the traction battery.
Each point therefore constitutes a node n.
The goal of the algorithm A* is then to find the path CI that will
minimize the fuel consumption of the vehicle.
The choice of the order for exploring nodes n is determined by
trying to minimize a function f which is the sum of a cost function
g and a heuristic function h, as shown by the following formula:
f(n)=g(n)+h(n)
where g(n) represents the quantity of fuel needed to arrive at the
node n from the initial node (start of the journey) over the best
available trajectory according to the choices of the charge or
discharge .DELTA.SOE to be applied to the battery over the
preceding sections, and
where h(n) represents an optimistic estimate of the quantity of
fuel remaining to be consumed with a charge or discharge .DELTA.SOE
that could be applied to the traction battery for passing from the
node n to the final node by considering the case of a linear
discharge of the traction battery from the node n.
The function f allows the algorithm to explore at each calculation
step the trajectory that both minimizes the cost for arriving at
the current node but also minimizes the remaining cost from this
node to the end of the journey.
Thus, the use of the function f encourages this algorithm to
explore the nearest trajectories to the optimal trajectory, this
limits the exploration of suboptimal trajectories, which enables it
to obtain good results in a minimum of calculation steps.
Once the optimal path is found (passing through the optimal points
of the reference curves CE.sub.j), the computer develops an energy
management setpoint according to the coordinates of the optimal
points P.sub.i.
This energy management setpoint is then used in the course of the
journey by the computer in order to track the trajectory, so that
the state of energy SOE of the traction battery follows the path CI
illustrated in FIG. 8.
A plurality of methods allow such tracking to be performed. One
example is notably well illustrated in patent application FR2988674
filed by the applicant, or in documents WO2013150206 and
WO2014001707.
The present invention is in no way restricted to the embodiment
described and represented, but the person skilled in the art will
know how to apply any variant that falls within its spirit.
In particular, instead of storing the parameters .PSI..sub.0,
.PSI..sub.1, .PSI..sub.2, .DELTA.SOE.sub.min, .DELTA.SOE.sub.max of
the reference curves, provision may be made for the computer to
store points globally characterizing the form of each reference
curve. This is then referred to as a map.
According to another variant of the invention, in the event that
the geolocation and navigation system does not know the value of an
attribute of a section of the journey, it may be provided that:
either the calculation of the sums of probabilities does not take
into account the values of the probabilities assigned to this
attribute, or the calculation replaces the unknown value with a
predetermined value.
* * * * *